Summary of MAC protocols by functionality, updates [34].
Abstract
This chapter describes the evolution of, and state of the art in, energy‐efficient techniques for wirelessly communicating networks of embedded computers, such as those found in wireless sensor network (WSN), Internet of Things (IoT) and cyberphysical systems (CPS) applications. Specifically, emphasis is placed on energy efficiency as critical to ensuring the feasibility of long lifetime, low‐maintenance and increasingly autonomous monitoring and control scenarios. A comprehensive summary of link layer and routing protocols for a variety of traffic patterns is discussed, in addition to their combination and evaluation as full protocol stacks.
Keywords
- Internet of Things
- sensor networks
- energy efficiency
- communications protocols
1. Introduction
The promise of the ‘fourth industrial revolution’ relies on the development and integration of the so‐called Internet of Things, cyberphysical systems and associated services and process improvements. The basis of the promise is the ability to instrument, connect, automate and remotely manage the majority of industrial systems and processes. The underlying assumption is that cheap, wirelessly communicating sensors and actuators can contribute to providing this capability, either autonomously or as part of a decision support system with a human in the loop.
In many cases, it is assumed that monitoring and control applications will require the use of devices that operate without persistent energy availability. Where no mains power is available, energy becomes a major constraint for applications expected to operate for increasingly long time periods. These periods are determined by the feasibility of maintenance of devices, with respect to both practicality and cost. Therefore, a major recent theme of research has been to attempt to achieve
Achieving energy neutral operation requires a comprehensive understanding of the application at design time and is seen as a ‘holy grail’ for networked embedded computing devices. However, applications tend to be characterised by heterogeneous performance requirements [8], deployment environments and criticalities. Therefore, there are few, if any, ‘one size fits all’ solutions. If one assumes that sensors and actuators are low cost with respect to energy in terms of sampling and information processing (which is not always true in the case of industrial applications [9]), communication is widely accepted to be the primary consumer of energy [10]. This energy consumption occurs when the radio transceiver is in an active state to send, receive and/or route packets. Assuming a worst‐case scenario whereby the radio transceiver is always active (a state which tends to require tens of milliwatts of power [11]), the obvious way to reduce energy consumption is to place the device in the lowest power mode available for as long as possible—a technique known as duty cycling [12], which can be applied to the radio transceiver (radio duty cycling, RDC) and the other components of the device. This is equally true for recent system on chip (SoC) solutions that integrate transceiver and microcontroller circuitry on the same chip.
However, the device must also be able to participate in a network, which necessitates the implementation of a communications protocol stack (Section 1.1.1). This is a long‐standing research area in the wireless sensor networks (WSN) community, which in the past 10–15 years has developed numerous energy‐efficient communications mechanisms that operate at and across various layers. The remainder of this chapter is dedicated to exploring the evolution of energy‐efficient communications primitives, explaining the inherent trade‐offs in the design space and providing a comprehensive description of the state of the art. The emphasis is placed on link and routing layers.
1.1. Wireless communication
There are several well‐known wireless communications technologies in popular use. These range from cellular, now on the verge of the 5th Generation (5G) [13], to WiFi (IEEE 802.11) and Bluetooth Low Energy (BLE) [14], among the most widespread Full coverage of wireless communication fundamentals is available in [15].
This chapter focuses on wireless communications protocols suitable for use with RF transceivers and SoCs typically considered ideal for low‐power WSN‐type applications, such as the (now obsolete) TI CC2420 [16], CC2538 (used in the recent OpenMote devices) [17], and the NXP JN5168. These are compliant with the IEEE 802.15.4 standard for low‐rate wireless personal area networks (LR‐WPAN) [18], which is responsible for underlying much of the research in this area and has found its way into numerous industrial standards and specifications including ZigBee [19], WirelessHART [20] and ISA100.11a [21].
1.1.1. Communications protocol suites
A communications protocol suite specifies how data should be formatted (i.e. in packets), transmitted and received (channel access), and routed in a network The practical implementation of a protocol suite is typically referred to as a ‘stack’.
While the higher layers of the stack benefit from having an understanding of, and in many instances real‐time information from, the lower layers, the most critical from an energy efficiency perspective are the physical layer (i.e. the radio transceiver itself) and the link layer, the latter of which is responsible for medium access control, and therefore, how long the transceiver remains in an active or sleep state. Energy‐efficient medium access control (MAC) protocols are discussed in Section 2. However, as discussed in Section 2, the selection of a suitable MAC protocol is heavily dependent upon the application, specifically with regard to the statistical properties of the traffic generated in the network, the network topology and environmental factors. A well‐designed application will take each of these factors into account at design time.
1.2. Hardware
The electrical characteristics of the hardware play a significant part in the overall energy efficiency of a networked device and the performance of a network. While a complete analysis of each component is beyond the scope of this chapter, it is worth highlighting where hardware mostly impacts the design of applications and implications for various layers in the protocol stack.
Firstly, selection of the radio itself is a key. From an application standpoint, basic quality of service requirements must be met, primarily bandwidth—otherwise the application is probably infeasible (without resorting to trickery in software, such as compressive or predictive sensing, and assuming this is satisfactory for an end‐user). The worst‐case scenario from an energy perspective is that the radio must be on 100% of the time. Therefore, once a wireless technology is selected (e.g. take IEEE 802.15.4), the designer sets about choosing a chip. The majority are reasonably similar in terms of their electrical characteristics, irrespective of the manufacturer. This makes life easier
Important performance characteristics are often affected by environmental conditions such as temperature and humidity. In the case of a wireless node, these have effects internally (i.e. on each device) and externally (i.e. the effects on the wireless medium), which both impact on the performance of a network. In the case of the device, temperature effects and component selection have a significant effect on relative clock drift, which must be taken into account when tuning and learning protocol parameters like guard times and phase offsets, respectively (Section 2). Understanding clock drift is essential to tightly configure networking parameters, such as guard times to ensure accurate synchronisation, and has been studied in [25] where the authors investigate the effects of environmental temperature on clock drift and propose strategies to help designers choose optimal resynchronisation periods for given accuracies, and in [26] where the authors study the impact of oscillator drift on end‐to‐end latency over multiple hops using varying capacitor accuracies and show how to determine optimal parameters to minimise energy consumption in duty‐cycled wireless sensor networks using low power medium access control techniques. It is also worth noting that temperature influences battery performance, particularly as temperatures reduce, where capacity is degraded and voltage is known to reduce. This is a relatively understudied area in terms of IoT/WSN technologies, but is likely to be very important where devices are deployed outdoors in cold environments.
2. Energy‐efficient medium access control
A large body of research exists concerning MAC protocols for WSNs. Notable examples of energy‐efficient implementations include A‐MAC [27], BoX‐MAC [28], HuiMAC [29], SCP‐MAC [10], ContikiMAC [30] and WiseMAC [31]. These MAC protocols are based on globally asynchronous, radio duty‐cycled (RDC) approaches, where the objective is to minimise the active time of the RF transceiver. Typically, trade‐offs are assumed to be inherent in the design of these protocols, and the Pareto‐optimal solution is sought when considering energy efficiency and application level or performance requirements, such as throughput, reliability and latency. In [32], the authors consider low data‐rate applications and attempt a tractable analytical approach to modelling latency and energy efficiency as functions of protocol parameters including duty cycle, slot duration and total slots, seeking to determine optimal settings for given workloads defined by application‐level parameters. They find that WiseMAC best balances energy efficiency and latency based on the scenarios considered, and attribute minimising protocol overhead through local synchronisation (also referred to as
These protocols, however, are just the tip of the iceberg (and are heavily oriented towards aggressively duty‐cycled, energy‐efficient scenarios for low data‐rate applications). In [34], Bachir et al. present a comprehensive taxonomy of MAC protocols according to the various techniques being used, classifying them according to the challenge they address. They describe the importance of understanding the statistical properties of the network traffic when selecting and tuning a MAC protocol, which is argued to be a more useful approach for the application developer. The authors classify the traditional ‘MAC families’ as
Functionalities | Protocols |
---|---|
Scheduled | TSMP, IEEE 802.15.4(e) [35], Arisha, PEDAMACS, BitMAC, G‐MAC, SMACS, TRAMA, FLAMA, μMAC, EMACs, PMAC, PACT, BMA, MMAC, FlexiMAC, PMAC, O‐MAC, PicoRadio, Wavenis, f‐MAC, Multichannel LMAC, MMSN, Y‐MAC, Practical Multichannel MAC, LMAC, AI‐LMAC, SS‐TDMA, RMAC, Reins‐MAC [36] |
Common active period | SMAC, TMAC, E2MAC, SWMAC, Adaptive Listening, nanoMAC, DSMAC, FPA, DMAC, Q‐MAC, MSMAC, GSA, RL‐MAC, U‐MAC, RMAC, E2RMAC |
Preamble sampling | Preamble‐Sampling ALOHA, Preamble‐Sampling CSMA, Cycled Receiver, LPL, Channel Polling, BMAC, EA‐ALPL, CSMA‐MPS, TICER, WOR, X‐MAC, MH‐MAC, DPS‐MAC, CMAC, GeRAF, 1‐hopMAC, RICER, WiseMAC, RATE EST, SP, SyncWUF, STEM, MFP, 1‐hopMAC, SpeckMAC‐D, MX‐MAC, IX‐MAC [33], ContikiMAC [30], LPP [37], RI‐MAC [38], A‐MAC [27], Flip‐MAC [39] |
Hybrid | IEEE 802.15.4, ZMAC, Funneling MAC, MH‐MAC, SCP, Crankshaft |
Another interesting development relates to the exploitation of constructive interference, which is contrary to CSMA mechanisms and seeks to benefit from concurrent transmissions. Flip‐MAC [39] and Glossy (not strictly a MAC protocol) [40], for example, make good use of this with regard to packet acknowledgements and efficient network flooding, respectively.
2.1. Standards and evolution
The IEEE 802.15.4 standard is one of the most important standards in WSN/IoT. First published in 2003, it specified the physical and medium access control mechanisms for low‐rate wireless personal area networks and became part of the industrial standards and specifications listed in Section 1. The medium access control layer specified in the standard is effectively a hybrid construct that allows the use of slotted or un‐slotted CSMA‐CA with optional guaranteed timeslots and packet delivery. It was designed to accommodate a range of topologies, including star and peer‐to‐peer, specifying device classes that allowed for reduced protocol complexity for ‘reduced’ function devices (RFD) opposed to ‘full’ function device (FFD) capable of communication with any device in the network. While this allows for relatively low duty cycles to be achieved, there is an inherent trade‐off between energy saving and latency and bandwidth. This was studied immediately, and simulations were used to illustrate the trade‐offs related to using the various modes, for example, in [41].
Within the Contiki community, a number of the same protocols were implemented and extended into standard link layers and radio duty cycling mechanisms in a cross layer fashion. Features from BoX‐MAC and X‐MAC, such as periodic wake‐ups and strobes, and WiseMAC, specifically the phase lock optimisation, were integrated to form the ContikiMAC protocol, which has shipped as a
TSMP is one of the most notable TDMA MAC layers developed in the last decade [43]. It uses synchronisation between devices to communicate in scheduled timeslots (allowing low‐energy radio management) and operates reliably in noisy environments by using channel hopping to avoid interference, with different time‐slotted packets sent on different channels depending on the time of the transmission. Therefore, the approach is suitable for applications that require relatively low‐power and high‐reliability performance, characteristic of industrial automation scenarios, and has thus been included in the WirelessHART and ISA standards (Section 1.1). More recently, it has become fundamental to the development of the IEEE 802.15.4e amendment to the standard, which is actively being included as a core technology in the IETF standardisation effort (e.g. Figure 1).
2.2. Design trade‐offs
It is important to understand the trade‐offs inherent in the link layer design choice. From an energy efficiency perspective, selection of a low‐power transceiver is critical, but selection of the appropriate link layer will depend on the application's performance requirements. All of the aforementioned protocols are
Table 1 shows that the area of MAC design for wireless sensor network applications has been comprehensively researched. As a result, many recently proposed protocols integrate and build upon ideas previously presented. The parameters of importance are now reasonably well known. CSMA‐CA protocols with arguably the best performance typically operate using request‐to‐send/clear‐to‐send (RTS/CTS) signalling, where networked devices periodically listen to the channel to determine whether any neighbours want to send packets, or alternatively begin to strobe RTS packets and wait for a CTS message from a listening neighbour if the device wants to transmit. The choice of what to send in the RTS packet is an interesting one, with many designers proposing to effectively send an entire data packet as the RTS strobe, such as in ContikiMAC, whereupon the receiving node sends an ACK having received the data payload. However, this can be suboptimal considering variable length data packets. It is argued in [33] that the use of a fixed‐length RTS packet can bring efficiencies when considering its relationship to strict timing parameters. Addressing information (to enable unicast, broadcast and/or multicast communication) is a key inclusion for such a packet.
Timing parameters for such protocols are bounded by performance characteristics of the RFIC (such as turnaround times between TX and RX modes) and the theoretical minimum times required to transmit, receive and act upon packets. Perhaps the most critical parameters are the
The RC is where a node samples the wireless medium for energy to determine whether or not there is an incoming packet. An optimal duration for the RC is tightly coupled with the time taken to listen for a CTS strobe during a wake‐up stream. Minimising the RC is a key to optimising energy efficiency, but this period is also closely related to reliability performance. Theoretical minimum values for length of the RC and the CTS listen period are calculated using the RXTX turnaround times for the transceiver, its bit rate and processing time.
The RCI is the interval between performing RCs, typically measured in Hz. It is possible to determine an optimal RCI with regard to energy efficiency, but this is detrimental to latency (where a latency is fundamentally lower bounded by the RCI and the number of hops over which a packet must travel to reach its destination). RCIs are often lengthened to reduce their impact on the quiescent power consumption of a device. Where large RCIs are used, for example, >0.5 Hz, it becomes more important to implement phase or offset learning (resulting from positive or negative relative clock drift) to ensure that energy consumption is minimised when beginning the next RTS/CTS wake‐up stream.
Transmitting nodes may or may not, depending on the application, have an enforced periodic data reporting interval. This is often referred to as the inter‐packet interval (IPI) in the literature and broadly reflects the frequency with which sensor data are transmitted from each device in the network. It is worth remembering that the IPI is not necessarily the same as the sampling rate of the sensor, where in many cases an aggregate or average values may be returned periodically (i.e. at the IPI rate of the node).
A factor less studied in the literature is the implementation of the CSMA algorithm itself. In [33], the authors demonstrate that a quantised approach to the implementation of back‐offs performs better with regard to energy efficiency and reliability than randomised approaches.
So called receiver‐initiated protocols have more recently been studied, whereby when a node wants to transmit a packet it waits for a neighbour (i.e. a potential receiver) to send a probe. Upon hearing the probe, the sending node transmits its packet. Examples of this approach are Low‐Power Probing (LPP) as introduced in Koala [37], RI‐MAC [38] and A‐MAC [27].
The physical and link layers determine the lower bound for energy efficiency with respect to point‐to‐point communication. When a network extends beyond a one‐hop (or star) topology, devices in the network must implement some routing functionality (usually referred to as Layer 3 or the NWK layer), to ensure that packets arrive at the intended destination, which is explored in the next section.
3. Energy‐efficient networking, data collection and dissemination
Routing algorithms are essential for every WSN as they define how packets flow within the network. Algorithms differ from each other based on their capabilities, effectiveness, amount of memory required to store the routing state, agility and energy awareness.
Routing protocols for WSNs can be categorised into three main groups depending on the functionality they provide: (
3.1. Data collection
Typically, WSNs have been deployed to collect data about certain phenomena in predefined geographic areas (sensing field). Nodes in such a network sample a sensor at a predefined rate and report the sensed data towards a sink node. The network may contain several base‐stations, in which case a node typically forwards data towards the closest base‐station only.
Two of the most common routing protocols are RPL is in fact designed to support any-to-any communication; however, very little work has been done in this regard, with early evaluations suggesting that it is not ready for actuation-type messages [45].
A primary challenge in the design of these protocols is defining the metric by which a node chooses its parent, via which the node will forward all packets. Early adopters of this approach used hop count to the base‐station as a metric [48]. Later, CTP used a new metric: Expected Transmission Count (ETX). ETX uses the number of transmissions required to deliver the packet to the destination without error. ETX depends on the quality of the link. Many protocols vary by how the quality of the link is measured and computed (e.g. based on RSSI, or a statistical measure of the number of packets lost, or a function of both). It has been shown that using this metric decreases the network traffic and leads to lower energy consumption.
Energy efficiency can be described as the ratio between the total number of packets received at the destination node (i.e. the base‐station, in the case of collection protocols) and the total energy spent by the network to deliver these packets. Due to the overhead of IPv6 protocol, researchers were concerned about the energy efficiency of the RPL protocol. However, it was shown that packet delivery ratio of CTP and RPL is very similar, while the overall energy consumption is only 3% higher for the latter [49]. Similar results were observed in a study focused on interoperability of RPL implementation for Contiki and TinyOS operating systems [50].
A disadvantage of building rigid routing trees is that the nodes along the path towards the base‐station have to transfer more data than other nodes, and hence their batteries deplete faster. This especially applies to the nodes closer to the base‐station. To tackle this problem, Lindsey et al. proposed
An alternative approach to creating rigid routing trees is the back‐pressure protocol (BCP) [52] presented by Moeller et al. In networks with BCP, the routing decision depends on the size of the packet queue and the packet rate between two nodes. Each node maintains a queue of packets, where a base‐station has a queue of zero length. A node forwards a packet to a neighbour only if the neighbour's queue is shorter than the queue of the sending node. The received packet is put on the top of the queue and in the next iteration forwarded to a node with a shorter queue. This can lead to more evenly spread network traffic while exploiting various routes towards the base‐station.
An older approach which tries to eliminate rigid routing trees is
Heinzelman et al. presented
One of the extensions of LEACH algorithm presented by Lindsey and Raghavendra,
Younis and Fahmy presented another LEACH extension called
Each of these hierarchical routing protocols scored three out of four points for energy efficiency in a large survey on routing protocols [56], meaning that these protocols achieved average packet delivery rates while choosing routes based on the residual energy, thereby prolonging the lifetime of the network.
3.2. Peer‐to‐peer routing
For networks deployed for the purpose of monitoring and continuous collection of data, peer‐to‐peer (P2P) communication is often not necessary. Each node only needs to know how to deliver data to one of the base‐stations. However, as WSNs become more common and serve a wider range of purposes, communication among nodes in the network will become more important. WSNs are not only used to collect data but also to react to the environment and control it via
Such P2P protocols may be categorised into five groups, depending on how they locate and forward messages to the communicate with a peer: (
3.2.1. Geographic routing
In geographic routing, each node is not addressed by its ID or IP address but by its geographic location. The routing decision is then based on the position of the node making the forwarding decision, the position of the destination node and the position of the neighbours of the forwarding node. The neighbour which is closest to the destination node is chosen as the next‐hop. As geographic routing heavily relies on the exact geographic position of the nodes, specialised hardware is required (e.g. GPS) and/or a localisation algorithm must be used. However, specialised hardware increases the price of the node, increases the energy requirements and is sometimes not very precise. Similarly, using localisation algorithms tends to lead to additional network traffic and may also be imprecise [57–59].
Among others, Karp and Kung proposed
According to the survey on routing protocols in [56], GEAR outperforms GPSR in terms of the packet delivery; however, it scores only two points out of four for energy efficiency.
3.2.2. Routing trees
In networks where P2P communication is based on
To tackle some of the disadvantages mentioned above, several improvements to the routing trees have been introduced. The key improvements are based on storing meta‐data on the nodes within the network. Dedicated nodes store meta‐data about all nodes in a sub‐tree rooted in given node. Then, a node can decide to route a packet down a tree without forwarding it to the root node. In RPL, the base‐station holds a routing table for the whole network. However, any node in the network, provided it has enough memory, can store a routing table for a sub‐tree rooted in given node. These nodes are referred to as
Duquennoy et al. presented an opportunistic version of RPL called
Mihaylov et al. use a similar approach in their
3.2.3. Hierarchical routing
In
At the centre of each cluster is a
The
3.2.4. Ad hoc routing
Unlike other routing algorithms,
The disadvantage of this approach is a very expensive path discovery. As the whole network is flooded with a request, this results in poor energy efficiency. Even though other approaches like routing via trees also rely on path discovery, the search in those networks is more directed and does not flood the entire network.
3.2.5. Routing based on routing tables
Approaches based on
Routing algorithms like RPL [23] use a subset of nodes to store the routing table and only for nodes that are in the sub‐tree rooted in a given node. Other approaches like
3.3. Dissemination protocols
The purpose of
Two of the basic techniques used are flooding and gossiping [73]. While in the case of flooding each node just re‐broadcasts every message it receives, in the case of gossiping, a node upon receiving a message randomly chooses a neighbour to which it forwards the message. The disadvantage of flooding is the implosion and duplication of messages, while the disadvantage of gossiping is a possible large delay in propagation of the message.
Heinzelman et al. proposed a family of
Braginsky and Estrin proposed
Both SPIN and RR scored the lowest mark for energy efficiency—one out of four—in the survey on routing protocols [56], due to their low packet delivery rate and not being energy aware when disseminating the data.
Levis et al. introduced probably the most popular dissemination protocol called
Kolcun et al. introduced the
3.3.1. Special case: low‐power wireless bus
The low‐power wireless bus (LWB) is a special case that considers multiple traffic patterns, including one‐to‐many, many‐to‐one and many‐to‐many traffic by exploiting the aforementioned Glossy mechanism to facilitate efficient and reliable floods [77]. It uses time synchronisation to manage access to the bus, where a global communication schedule is maintained (computed online based on immediate traffic) and flooded periodically to nodes (thus avoiding relative clock drift). The authors demonstrate that the LWB is comparable to or outperforms a number of state‐of‐the‐art stacks with regard to many‐to‐one (i.e. collection) traffic, adapts well to varying traffic volumes, significantly outperforms contemporary approaches in terms of many‐to‐many, is robust to inference and intermittent node participation, and supports mobile nodes as source or sink network devices (Table 2).
Functionalities | Protocols |
---|---|
Collection | CTP [44], RPL [23], BCP [52], LEACH [53], HEED [55], PEGASIS [54], LWB [77] |
Dissemination | SPIN [74], RR [75], Static Attribute Propagation [72], RPL [23], LWB [77], Trickle [76] |
Peer‐to‐peer | GEAR [62], GPSR [60], Geometric Ad hoc Routing [61], ORPL [64], Innet [66], AODV‐BR [69], Dragon [72], LWB [77], Chaos [78] |
4. Full stack implementations
4.1. IPv6 over LR‐WPAN
With arrival of IPv6, researchers set about implementing it for sensor networks. This was met with several challenges. Because the main usage domain of IPv6 is Ethernet, to cope with increased Internet traffic, the maximum transmission unit (MTU) was increased from 576 to 1280 bytes, when compared to IPv4. IPv6 addresses are 128‐bit long, and the standard IPv6 header size is 40 bytes. This is in strict contrast with the IEEE 802.15.4 standard whose throughput is limited to 250 kbps and the length of the frame to 127 bytes. The standard supports two addresses: short 16‐bit and EUI‐64 extended addresses. With link headers included, the effective size of the payload could be as small as 81 bytes, which make IPv6 headers seem too large.
In 2007, Mulligan and an Internet Engineering Task Force (IETF) working group published a proposal on how to transfer IPv6 packets in low‐rate wireless personal area networks. A new protocol called 6LoWPAN [79] was introduced. The aim of the working group was to define a stateless header compression that would decrease the header size so it can be used with the IEEE 802.15.4 standard. The reduction was achieved by introducing four basic header types: (
When an address needs to be included in the header, 6LoWPAN supports either 16‐bit short addresses or full 64‐bit addresses. These addresses are then translated to full 128‐bit IPv6 addresses by a border router. The border router is a router that enables communication between a WSN and the Internet.
If a node needs to send a packet whose size is larger than the size of the payload of 802.15.4 frame (107 bytes), 6LoWPAN defines a fragmentation header which allows the node to split the original datagram into several packets. The header includes the size of the original datagram as well as the ordering number. Fragmentation is also necessary as the specification of IPv6 requires support of a minimum MTU of 1280 bytes.
6LoWPAN supports two types of routing: (
In the route‐over approach, the routing decision is done on the network layer (layer 3) and each node acts as an IP router. Each link/hop is considered to be an IP hop too. If a packet is fragmented, then all fragments are first reassembled on the next hop neighbour and the packet is passed to the network layer. The network layer decides whether the packet should be processed on the node or forwarded to a neighbour. To make this decision, the node has to either store the routing table which maps the destination address to the next‐hop address or the packet itself has to contain this information. In route‐over approach, each node must have enough memory to reconstruct the packet, and all fragments are routed via one path only. On the other hand, if a fragment is lost during the transmission, the whole IP packet must be resent over one link layer hop only.
Recalling Figure 1, a full stack implementation requires some higher and lower layer primitives. At the application layer, the IETF has worked to standardise the so‐called Constrained Application Protocol (CoAP), which is essentially a RESTful (Representational State Transfer) protocol that uses a small subset of HTTP commands, and more recently TSCH at the link layer, detailed earlier. To‐date, there are no comprehensive evaluations of the energy performance of the entire stack. However, there are several which evaluate snapshots of the stack, or subsets thereof (e.g. by layer), such as in the case of CoAP in [80], where the authors show that CoAP is efficient when implemented over RPL, 6LoWPAN and ContikiMAC (and reiterate that the key efficiencies are to be gained at the link layer), and 6TiSCH [81], where a realistic energy model presented for TSCH demonstrated that under certain conditions, sub‐1% duty cycles are demonstrable for real and simulated networks under reasonable traffic loads.
4.2. Composable stacks
Many of the protocols described thus far have open source, modular implementations available in the libraries of the various operating systems. Therefore, they can easily be composed to suit an intended application scenario. We know that performance and appropriate selection depend on application level requirements and statistical properties of the network traffic generated by that application. Therefore, there are very few studies that explore in‐depth full‐stack implementations on per‐application bases. However, there are a number well‐documented implementations that comparatively evaluate performance such as in [77], where the authors compare the performance of LWB against Dozer (a highly efficient TDMA‐based data collection protocol) [82], CTP+A‐MAC and CTP+LPL, under a variety of conditions. CTP+A‐MAC, LWB and more recently Chaos [78] are representative state‐of‐the‐art stacks from the research community that rival the standards‐based stacks which tend to adopt something very similar to the 6TiSCH approach (Figure 1), for example. While many of the protocols described so far have been implemented in the longer‐standing operating systems developed in the research community, a number of more recent such operating systems have emerged, such as OpenWSN—https://openwsn.atlassian.net/wiki/, and RIOT—https://www.riot‐os.org/#features, which provide implementations of the standardised stack for a variety of recent hardware development platforms.
4.3. Energy analysis
One of the most comprehensive evaluations of a relatively complete stack is presented in [44], where CTP is run on a number of heterogeneous test‐beds, over a number of link layers, for a variety of inter‐packet intervals (typically determined by the application scenario) and at a variety of radio frequencies on various channels. While the evaluation shows that the combination of beaconing and data path validation used in its design is robust over a variety of physical and link layers, the performance characteristics still do not quite meet those needed for ultra‐long‐lived, large (i.e. extremely dense) and highly reliable applications. The authors also leave open the question of whether these methods are suitable for distance vector algorithms synonymous with
Generally speaking, it is extremely difficult to validate or comparatively evaluate the energy performance of a protocol stack relative to another. The use of simulators and non‐standard test facilities (e.g. community‐known test‐beds set‐up with arbitrary configurations) contribute to this problem. This could be mitigated against by having a set of standard simulation and test‐bed configurations against which to benchmark protocols at each layer, and in combination. This has been recently alluded to in the literature in [83], wherein the authors conclude that there is insufficient knowledge available for a majority of the community when it comes to trialling experiments on real‐world facilities such that they can be trustworthy, reproducible and thus independently verifiable.
5. Conclusion and future directions
Practical, energy‐efficient wireless communications protocols have been comprehensively studied and documented in the literature in the preceding decades. We have presented a comprehensive summary review of the state‐of‐the‐art concerning link and routing layer technologies developed during this period suitable for constrained wireless sensor network, IoT and CPS applications.
A great deal is known about their limitations and the trade‐offs inherent in their selection and implementation. It is arguable that fundamental performance limits have been reached in the design of link layer technologies for contemporary radio transceivers. For this reason, and understanding the time‐varying nature of the wireless medium, routing protocols are being developed by the standards bodies that take into account lower layer parameters in their design (e.g. IEEE 802.15.5). These may include link quality, residual energy and dynamic energy availability in the case of energy harvesting devices.
Devices are incrementally more efficient, and their inter‐networking based on link and routing layer technologies is maturing to the point where protocols can confidently be selected where certain performance requirements must be satisfied. We tend to readily trade energy efficiency against reliability and determinism for industrial and high‐criticality applications. This is problematic, however, because many potential application scenarios are dismissed as economically infeasible due to high network maintenance costs. Simultaneously, as devices and protocols maximise efficiency, the gap with feasible energy harvesting from devices’ ambient environments is reducing. Coupled with other techniques and technologies, like compressive and predictive sensing, ultra‐low power wake‐up radio circuits and so on, there is an emergent design space—where applications can be holistically co‐designed with regard to energy. It is almost certain that such approaches will be investigated in the short to medium term, which will result in the economic feasibility of a range of new connected monitoring and control applications.
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Notes
- Full coverage of wireless communication fundamentals is available in [15].
- The practical implementation of a protocol suite is typically referred to as a ‘stack’.
- RPL is in fact designed to support any-to-any communication; however, very little work has been done in this regard, with early evaluations suggesting that it is not ready for actuation-type messages [45].