Syed Asad Hussain, Muhammad Mohsin Mehdi, and Imran Raza
5.2 CURRENT CURRENT TECHNIQUES TECHNIQUES FOR FOR ENERGY-EFFICIENT ENERGY-EFFICIENT Io IoT T
5.2 CURRENT CURRENT TECHNIQUES TECHNIQUES FOR FOR ENERGY-EFFICIENT ENERGY-EFFICIENT Io IoT T
Existing solutions for energy efficiency in IoT devices can be categorized based on the type of pro-tocols and standards they adopt in different working scenarios. Notably, available methods focus on
93 93 Energy-Efficient Routing Protocols for Ambient Energy Harvesting in the Internet of Things
enabling either low-energy routing protocols or smart scheduling among IoT devices. Other meth-ods may involve improving energy conservation by enhancement to physical layer concepts in IoT infrastructure. The following text classifies these techniques on the above-stated notion, highlight-ing the significance of each method.
5.2.1 I
OT R
OUTINGP
ROTOCOLSFORL
OWE
NERGYC
ONSUMPTIONAd hoc and sensor networks use various routing protocols and mechanisms, and IoT systems use the same routing mechanisms. The three main factors that may affect the routing in the loT are consumption of energy in sensors, the node’s mobility, and the kind of IoT middleware that is used.
The 6LoWPAN (Kushalnagaret et al., 2007) routing protocol is used to realize an IoT ecosystem, as shown in Figure 5.1. The Internet Engineering Task Force (IETF) has identified the mechanism for routing of data for non-IP sensors in the Internet. This mechanism of routing depends on IEEE 802.15.4, which is appropriate for sensors with low power. The configuration of 6LoWPAN is com-posed of a series of reduced function sensors. Such sensors must be connected with full-function sensors in order to complete the topology. Furthermore, a network point is used as an entrance to another network, which means that there is specifically a gateway that acts among domains of vari-ous networks. The 6LoWPAN stack includes physical, media access control (MAC), adaption, IPv6, transport, and application layers, which are required for networking functions.
The physical layer of IEEE 802.15.4 works to effectively provide 27 channels depending on different frequencies or data rates. This medium of IEEE 802.15.4 is managed by the MAC layer through the Carrier-Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. The MAC layer also guarantees the association, disassociation, and synchronization of a device. The adaptation layer adapts various IP packets and fits them into the format appropriate for the network.
Another purpose of this layer is to fragment IPv6 packets into MAC frames. It is ensured at this layer that the process of fragmentation takes place successfully. The User Datagram Protocol (UDP) is used for delay-sensitive transmission by the transport layer. The reduced function sensor starts routing (in 6LoWPAN) when it needs to transmit a packet to another IP sensor.
As data chunks are assembled at the gateway, a fully functional sensor needs to get connected to the reduced function sensors and the former is also liable for transmitting the data. The IP address is used by the gateway to find the domain of the remote IP sensor. Furthermore, it has adopted the 6LoWPAN Ad Hoc On-Demand Distance Vector Routing (LOAD) for routing. Messages for route request (RREQ) and route reply are used by LOAD. The link layer notification messages serve a function that authorizes receiving MAC messages. A mesh topology is created and runs on fully functional devices. A route can be selected by LOAD if it has fewer hops from source to destination.
IPv4/IPv6 tunneling Gateway
6LoWPAN—IPv4 tunneling
IPv4 IPv6
Internet IPv6
IPv6 IPv6 servers 6LoWPAN network
Application layer Transport Reliable layer
UDP layer TCP
IPv6 layer 6LoWPAN—Application layer
IEEE 802.15.4 MAC layer IEEE 802.15.4 PHY layer
Other??
Network MAC Physical
FIGURE 5.1
FIGURE 5.1 6LoWPAN network stack.
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6LoWPAN uses a hierarchical routing (HiLow) protocol in which devices either join an existing parent or become a parent itself in the hierarchical structure.
As discussed in Tran and Thuy (2011), 6LoWPAN is suitable for networks having high process-ing ability. The Routprocess-ing Protocol for Low-Power and Lossy Networks (RPL) (Winter et al., 2012) is developed for devices limited in power, computation, and memory capabilities. RPL is a distance vector routing protocol based on IPv6 that computes all the distances and opts for the shortest dis-tance toward the destination. Similarly, Energy-Efficient Probabilistic Routing (EEPR) is another solution that works like AODV, but sending an RREQ packet depends on a forwarding probability determined by the residual energy and the end-of-transmission (ETX) metric.
Chen et al. (2012) have proposed a Context Awareness in Sea Computing Routing Protocol (CASCR), which generates intelligent decisions based on interactions of IoT devices at the local level. CASCR binds a state and a set of operations to every IoT device. The identified possible states can be full working, serving, single working, sleeping, and hibernating. The possible opera-tions pertain to gathering information, transmitting information, applying information fusion, and generating a control operation. In particular, CASCR estimates the new state of every device using Markov chains by defining the new state as a function of any device’s history in an IoT environment.
A device having a routing request should transmit it to its first-hop neighbors, which are given a context data table specifying the network topology. Data is sent between all the devices that have a request to route and are waiting for their turns. The data is sent from a neighboring device to another through hops. The superior nodes, along with subordinates, neighbors, colleagues, and disabled devices, successfully create the network topology. Researchers in the past have discussed the perfor-mance of traditional routing protocols for ad hoc networks such as Dynamic Source Routing (DSR), Ad hoc On-Demand Distance Vector (AODV), and Optimized Link State Routing Protocol (OLSR) in an IoT environment, focusing on routing overhead, throughput, and average end-to-end delay.
5.2.2 E
NERGY-E
FFICIENTS
CHEDULINGAMONGI
OT D
EVICESAccording to a study by Gartner (2013), the number of devices connected to the Internet will increase to 26 billion by 2020. These devices will consume a considerable amount of energy to accomplish various tasks and yield enough electronic garbage. This will result in a situation where there are power failures because control on its consumption will be difficult in the future. There is a need for adoption of new ways through which green communication can be deployed across the IoT network. Energy consumption in heterogeneous IoT devices affects the cost and availability of an IoT network. Consuming a lot of power has been an issue for a very long time, and it is likely to become more prominent with the passage of time. To overcome such issues, we need to use more energy-efficient hardware components and software to reduce overheads. Energy conservation in devices is also achieved through efficient scheduling algorithms with minimum response time, which is the time required by a processor to release a job and complete it (Albers, 2010).
Scheduling of sensors can be done by using the energy-efficient algorithm (Abedin et al., 2015), discussed as follows. It is integrated into the configuration of the network and saves energy. This is a generic energy-efficient algorithm that serves as a foundation for designing new energy-efficient routing protocols (Abdullah and Yang, 2013; Park et al., 2014; Raniet al., 2015; Chelloug, 2015). Algorithm:
Algorithm:Energy-efficient schedulingEnergy-efficient scheduling
1. Initialize Sleep_timer SLt. Sleep_energy Esl sensing period St,
consumed_enery E elecenergy_reservoir ei, maximum_energy Emax,
read_value, transmission_value, command_value, temp_value, Received data packet DRK ,
Transmitted data packet D TK
2. While stage ≠ current_stage && ei ≤ Emax
95 95 Energy-Efficient Routing Protocols for Ambient Energy Harvesting in the Internet of Things
3. Read stage
4. Select Case of stages
5. Case 1: Current stage = ‘on_duty’
6. While(read_value > −1)
7. D= read_value RK //sensing and
reading sensor value
8. End of loop
9. While(transmission_value> −1)
10. DTK = transmission_value //transmitting
sensor value
11. End of loop
12. Case 2: Current stage = ‘pre_off’
13. Read new_stage
14. If new_stage≠ current_stage
15. current_stage = new_stage
16. Transmit ACK to sink node
17. Else
18. current_stage = ‘off’
19. Case 3: current_stage = ‘off’
20. Hibernate mode with sensing capability
21. While St > 0
22. If temp_value = read_value
23. St = 0
24. Current_stage = ‘pre_off’
25. Else
26. Set E sl //
enter sleep mode
27. While SLt > 0
28. Power down mode with S Lt = 0
29.
30. End of loop
31. End of loop
32. End case
33. End of loop
Sleep_timer SLt is the time allotted to the specific node in the pre-off-duty state. Sleep_
energy E sl is the energy of the hibernating node. Sensing period S t is the time period of the off-duty node, when it is only sensing the environment. Consumed_enery E elec is the energy used by a specific node during receiving and transmitting the received data packet D RK and transmitted data packet DTK , respectively. Energy_reservoir ei is the energy available to all nodes through ambient energy sources. Maximum_energy E ma x is the amount of energy that a node can use.
In the on-duty state, a sensor node will sense, receive, and transmit the data. These sensors may behave as a relay node (RN) or sink node based on their capabilities. The pre-off-duty state follows the duty state whenever the device is idle for some time. This state can switch between both on-duty and off-on-duty whenever it needs to do so. In the pre-off-on-duty stage, a device only receives and transmits the required commands from the sink node. The off-duty state is constituted by hibernate, sleep, and power-off states. In the hibernate state, a device has nothing to do except sense the envi-ronment, and for this purpose, the device may use small energy resources as well. In the sleep state, the device instantaneously stops working but starts again when required to resume. The power-off state puts the device into a deep sleep (Abedin et al., 2015).
Figure 5.2 illustrates how this scheduling algorithm works. In sending a message from node 1 (sink node) to node 7, only these two nodes are required to be fully operational. For intermediate nodes 3 and 5, they are in pre-off-duty states to save power, as their job is to receive and forward.
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Node 6 can be powered off, as it is not participating in communication. Node 4 is in either the hiber-nate or sleep mode, as it will only be used in the case of failure of node 5.
5.2.3 M
INIMUME
NERGYC
ONSUMPTIONC
HAIN-B
ASEDA
LGORITHMFORW
IRELESSS
ENSORN
ETWORKSThere are many factors, such as humidity in the air, temperature, and interference, that affect the transmission of sensor nodes. These factors render the wireless sensor networks (WSNs) with dynamic routing capability pretty much unstable and useless for large-scale networking. In a dynamic routing sensor, nodes exchange data about their location. Consequently, overhead is increased, thus increasing power utilization. For the same reason, this type of routing is not feasible for IoT networks. Furthermore, components involved in the composition of IoT networks are least mobile with consistent topology; therefore, a dynamic routing configuration is of less advantage over its static counterpart.
Figure 5.3 shows several nodes that constitute a hierarchical framework according to various parameters. The nodes that are presented in this topology are static, and their routing is static as well. The lower layers are composed of normal nodes, cluster heads (CHs), cluster coordinators (CCOs), and RNs. The uppermost convergence layer includes BSs having Internet connectivity.
Nodes in the lower layer sense and transmit data to their respective RNs. Afterwards, RNs pass the data to their concerned CHs. The load on CHs and CCOs is balanced by passing the data from CHs to upper-layer CCO. Afterwards, the data is handed over to the upper-layer CCO, and the same con-tinues until the data is transmitted to the BS at the uppermost layer. On the local level, information is sent using RNs, and neighboring clusters communicate through the CHs and CCOs only. This entire deployment maintains the energy efficiency and scalability in IoT due to static routing and the simple architecture of IoT components. A lot of energy can be effectively saved by placing IoT components above this framework.
5.2.4 E
NERGY-C
ONSERVINGS
OLUTIONSFORA
REA-S
PECIFICI
OT
Different types of wireless technologies, such as 3GPP, LTE, LTE-Advanced, Wi-Fi, ZigBee, and Bluetooth, can serve as vehicle to provide connectivity among IoT devices and gateways or servers.
conserving issues in IoT devices are also closely linked to wireless technologies. Energy-conserving issues arise in numerous manners, depending on the category of wireless radio access technologies, for example, methods for controlling overload or congestion within the network, methods to adjust duty cycles, and allocation of uplink or downlink radio-frequency resources in an energy-efficient manner.
On duty Off duty/power off Pre-off
Off
Duty/sleep/hibernate St = 0
St St
Elec 1 Emax
3
6
7
4 5
FIGURE 5.2
FIGURE 5.2 Generic schedule algorithm for energy efficiency.
97 97 Energy-Efficient Routing Protocols for Ambient Energy Harvesting in the Internet of Things
Wireless wide area networks (WWANs) have vast commercial usage. It is about time that they become an integral part of the generic IoT, just like WSNs. Wi-Fi-based Internet is an integral part of our lives, carrying various applications in almost every field. A BAN is the best example of a wireless personal area network (WPAN).
5.2.4.
5.2.4.1 1 Energy-Conserving Energy-Conserving Solutions Solutions for for WWAN-Based WWAN-Based IoIoTT
An important concern in adopting 3GPP LTE for the IoT is the huge number of IoT devices. The problem of overload or a congested radio access network or core network (CN) arises when both IoT devices and user devices access the network for data transfer at the same time. The problem is going to become uncontrollable as the number of devices increase in the coming years, be they human-to-human (H2H) or machine-to-machine (M2M) devices. The problem of overloading can indirectly affect IoT devices for their energy consumption. High network utilization is going to cause delay and loss of IoT data packets, and hence more battery power will be consumed in retransmission.
In addition to congestion-related issues of energy consumption, different factors, such as time, fre-quency, and transmit power, are considered for energy-efficient allocation of radio resources to IoT devices. There is a need to control multiple devices from accessing the system at the same time because this will also reduce power consumption. Figure 5.4 shows a typical ar rangement of use of a WWAN in an IoT environment.
Energy consumption is reduced through adaptive learning in fault-tolerant routing. As soon a fault is detected in the current path, the algorithm switches to the next available one with the highest goodness value (Misra et al., 2012). To conserve energy, all nodes lying on the unused path are put to sleep. Machine-type communications (MTC) are commonly used in 3GPP networks (Universal Mobile Telecommunications System [UMTS] and LTE)(Cheng et al., 2012). These are automated applications, which comprise communications between machines and devices (sensors) without human intervention. These devices generate a large amount of signaling traffic, which creates con-gestion and overload random access network (RAN) and CN. Although various content resolution mechanisms have been proposed in the past, none of them give satisfactory results. In RAN overload
NN
C l u s t e r 1 C l u s t e r 2 C l u s t e r 3
RN CH
CCO
Base station
FIGURE 5.3
FIGURE 5.3 A multitier IoT framework. NN, normal nodes.
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control method overload(Cheng et al., 2012), congestion or overload is controlled by randomly dis-persing the load to different time slots. This is called the push method. The other approach is the pull method, in which a polling-based access mechanism is used (Cheng et al., 2012). In another method to prevent overloading(Singh et al., 2012), an M2M or MTC gateway helps in saving energy by queu-ing the data until the M2M device wakes up at the beginnqueu-ing of next power cycle. After wakqueu-ing up, the M2M device handles all queued data at the M2M gateway and goes back to the idle mode later.
5.2.4.2
5.2.4.2 Energy-Conserving Energy-Conserving Solutions Solutions for for WLAN-Based WLAN-Based IoIoTT
A lot of power is consumed whenever an IoT device uses Wi-Fi to reach the CN of 3GPP and finally the servers due to congestion. When IoT devices use Wi-Fi to extend the area of operation, more power is consumed for multihop topologies due to severe collisions in multihop communications.
Further research and standardization are necessary to evolve a mechanism for collision avoidance in multihop communication.
Figure 5.5 shows a typical example of a WLAN setup for an IoT environment. A control to stop multiple devices starts accessing the system and, at the same time, will also reduce power consumption.
The major challenge faced is congestion or o verload. For this purpose, an offset listen i nterval algorithm was proposed in Abdullah and Yang (2013), which had the purpose to recover power loss. Further, we should be able to reduce the traffic so that the delay in the network can be reduced for the users and devices. To carry out this process, the wake-up time of the device is deferred in a random fashion, resolving congestion problems to some extent. It involves waking up the devices such that buffered data packets are transferred. All these packets will be sent to
Hardwire ISP/Server base
FIGURE 5.5
FIGURE 5.5 WLAN deployment example in IoT. ISP, Internet service provider.
Application and data centers CER
AT and T Mobility network
IoT devices
FIGURE 5.4
FIGURE 5.4 Typical WWAN connection in an IoT environment. CER, Cellular Enabled Router.
99 99 Energy-Efficient Routing Protocols for Ambient Energy Harvesting in the Internet of Things
the devices involved in the network in a timely fashion. Such mechanisms ensure efficient and effective performance of the network.
5.2.4.3
5.2.4.3 Energy-Conserving Energy-Conserving Solutions Solutions for for WPWPAN-Based AN-Based IoIoTT
As shown in Figure 5.6, a WPAN establishes connectivity between battery-operated constrained IoT devices. Bluetooth Low Energy (BLE) is a commonly used WPAN technology for IoT setups.
Different researchers (Park et al., 2014) have offered a BLE implementation and evaluated its per-formance as comparable to that of ZigBee/802.15.4. Energy is consumed during a master–slave discovery process, as the master and slave devices are not always in a connected mode. A master device searches for available slaves for connection simultaneously along the slave devices, which advertise their availability to the master. Energy used after establishing a connection is also con-sidered, and parameters related to energy, such as transmission and reception, along with the inter-frame spaces, are analyzed.
A neighbor discovery mechanism multicasts a high number of messages for IPv6 over BLE, con-suming higher energy in BLE-based IoT devices. A basic solution to this problem is to consider a neighbor discovery mechanism optimized to ensure that a node is removed from the neighbor cache as its lifetime is expired. To reduce the transmission of neighbor solicitation messages from other nodes, entries regarding nodes are kept in the neighbor’s cache.