Wireless sensor networks
Wireless Sensor Networks: Maintenance-Free or Battery-Free?
Two recent major technology waves were the cell phone and wireless Internet (Wi-Fi). Now there is a third wireless wave coming: wireless sense and control networks that can connect and control all kinds of equipment in our buildings, homes and businesses.
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Today, we are entering this third wireless wave. Also known as “The Internet of Things,” the third wave utilizes wireless sense and control technology to bridge the gap between the physical world of humans and the virtual world of electronics. Sense and control networks do not enhance human communication. Instead, they allow sensors to interact with actuators, creating a more dynamic world and avoiding error-prone, monotonous and costly human intervention. However, the strength of wireless sensor networks can only be fully achieved when the wiring for both the data communication and the power supply is eliminated.
The standard wireless sensor network solutions that are available today only solve a wiring problem in sensor applications: making networks easy to install. However, ultra-low-power wireless network solutions can also address the maintenance problem inherent to networks with a high number of nodes and a limited battery life. For example, a network of 4,000 nodes and a battery life of 10 years, means that on average 1 battery per day needs to be changed.
Because the true cost of wireless sensor networks has shifted into the area of the maintenance cost, wireless sensor networks that gather industrial or machine data have yet to become a cost-effective solution. The majority of the sensors used are still battery powered; these batteries require regular changing and or recharging. In addition, reintegrating the downed nodes after battery maintenance, further adds to this onerous labor expense. To avoid these high costs, the industrial sector and its applications require self-powered nodes that require very little power to operate for long periods of time. This is generating the rapidly emerging opportunity for ultra-lower-power wireless and energy harvesting.
As you can imagine, there are many different types of applications that will be able to benefit from ultra-low-power wireless sensor networks. These include monitoring of temperature, vibrations, humidity, position, tank levels, etc. in industrial plants and manufacturing. They can also be linked to the control and actuation of HVAC systems, storage, robot movements, temperature control, etc. But there are many others that are not so obvious. For example, agricultural applications now benefit from the use of wireless sensor networks when temperature sensors or soil moisture sensors are used for remote monitoring of test fields, vineyards or green houses and to control irrigation and fertilization.
For many real-world applications, the third wave of wireless–ultra-low-power wireless sensor networks–will provide many advantages including the cost elimination of hard-wiring, the enhanced flexibility in constricted or dangerous areas, ease of installation, increased safety and reduced maintenance costs of sensor deployments.
The challenge of designing wireless sensor applications is not limited to deploying reliable wireless communication. Power management is an even bigger challenge. This should not be a surprise since the real benefit in wireless communication is primarily to avoid the wiring cost, so the data cables as well as the power cable need to be eliminated.
The biggest technical challenge for developing ultra-low-power sensor networks is managing the energy consumption without reducing range or functionality, like speed and standards compliance. The resulting elimination of battery replacement will then simplify maintenance and provide a higher level of ease of use and safety.
It is obvious that current consumption–milli-amps–and duty cycling are important in wireless sensor networks. However, minimizing current consumption is only part of the solution. Five other essential issues are key to developing low-power wireless sensor applications, the first of which is low-power Wireless Mesh Routing.
One of the most dramatic differences between wireless sensor communication technology and other well-known wireless technologies is the ability of sensor nodes to forward messages from other nodes further down a communication chain. This is called mesh routing or multi-hop networking. Mesh networking is an effective and reliable solution for spanning large infrastructures beyond the range of what a single wireless link can do.
In a low-power mesh network, all the nodes, including the mesh routing nodes, operate in low power. Figure 1 depicts how low-power routing works when Node A wants to send a message to Node C, through Node B. All nodes in the pictures are low-power nodes and are in sleep mode most of the time.
The breakthrough lies in synchronizing the sleep/wake-up cycles of the nodes to each other. This means a node wakes up when it can expect a message from a neighbor node. This works through very precise synchronization of the transmitting and receiving nodes. As a result, the routing nodes will also be in a nearly powerless sleeping state most of the time, achieving ultra-low-power operation. The more accurately the wake-up schedule can match the communication expectations, the less power is consumed by unnecessarily long wake-up periods.
The idea is to let a node be awake in certain “slots.” A node cycles through a period when it is asleep and a period when it is awake. For nodes to be able to communicate with each other their “awake” slots need to overlap, meaning that they have to be awake at the same time. This requires an innovative approach for synchronizing “time” between nodes. The accuracy of the time synchronization will determine the minimum time a node needs to be awake.
The repetition rate of the awake slots is predefined for the network and depends heavily on the required responsiveness of the network. For example, how often does a sensor value need to be read out.
Another critical design parameter is peak current. When closely examining the power consumption behavior of electronic circuits, it becomes apparent that what looks like a flat current curve at first glance, actually bears more resemblance to a mountain range with peaks and valleys. When certain functional blocks become active, they cause a peak. When two functional blocks switch on simultaneously, they cause a peak of double height. The secret in reducing the peak power lies in carefully managing when the functions are turned on and off and avoiding double peaks at all times.
When an energy source has dried up, the electronics cannot communicate and are dead to any meaningful purpose. They need a way to fail gracefully. Loss of power can be a normal event such as a solar cell at midnight or an exceptional condition such as a depleted battery.
In both cases the power problem can be dealt with, provided the application is intelligent enough to detect the upcoming problem before the energy source has completely dried up. During this last breath the device should perform a number of actions to inform its environment of the situation, transmit some critical data and put itself in a state that allows fast recovery when the power is restored.
By using a built-in Graceful Power Failure function, the system is able to monitor various types of low-power energy sources including batteries and solar cells. They carefully monitor the state of the power circuits and raise different levels of alarms ranging from an early warning to near-death. These alarms are escalated to other parts of the system such that they all can move into a state that fits the alarm condition.
Wireless chips are usually quoted on their power consumption in receive and transmit mode. However, in order to achieve low power, the devices are duty-cycled, moving between alternate states of sleeping and being awake. The longer the battery lifetime needs to be, the longer the device will sleep between wake-up periods. Unfortunately, electronic circuits are never really “sleeping.” Although the powered-down circuits are not doing anything meaningful, a small leak current flows through the transistors. The leakage can amount to several 10s of µA. Sleep current is not usually considered an important design factor, but it becomes extremely important when designing a circuit to live for 5 years or more on a battery, sleeping most of its life.
Finally, there is the matter of wake-up time. As duty-cycling is achieved by switching off as many parts as possible during the sleep state, all these parts need to be re-activated when returning to the active state. Voltage regulators need to settle, clock oscillators need to start up, digital electronics that were powered down need to be put in a known state. In order to avoid unnecessary energy consumption, it is very important to make the start-up times as short as possible without compromising current consumption in normal operation. Smart sequencing of such start-up cycles is crucial for ultra-low-power operation.
The reliability of a wireless sensor network is related to the availability or absence of a communication path between two wireless devices. The most important enemy of reliability is wireless interference originating from other users of the same frequency band. The most notable interferers for IEEE 802.15.4-based devices that operate in the 2.4 GHz frequency band are Wi-Fi transceivers. Most interferers will not fully block out an IEEE 802.15.4 device, but will cause some wireless packets to get lost regardless of the network stack operating on top. The industrial standards provide a mechanism that allows packet losses to become evenly spread out over time, even if the number of lost packets will not substantially decrease.
To further increase reliability some companies have added antenna diversity to the wireless sensor network communication features. Antenna diversity is based on the use of two antennas to improve the quality and reliability of a wireless link. Often there is not a clear line-of-sight (LOS) between transmitter and receiver, and the signal is reflected along multiple paths before finally being received. Each of these bounces can introduce phase shifts, time delays, attenuations, and even distortions that can destructively interfere with one another at the aperture of the receiving antenna.
Antenna diversity is especially effective at mitigating these multipath situations because multiple antennas provide a receiver with several observations of the same signal. Each antenna will experience a different interference environment. Thus, if one antenna is experiencing a deep fade, it is likely that another has a sufficient signal. Collectively, such a system can provide a robust link.
Figure 3 illustrates how diversity antennas work. In receive mode, the signals are fed to the diversity switch, which is controlled by a signal in the DSP inside the transceiver. The DSP controls the switching (by control signal C1) and will select the best signal antenna. In transmit mode, one of the control signals C2 or C3 enables one of the power amplifiers PA1 or PA2. B represents a balun that acts as an impedance transformer.
For wireless sensor transceivers, the dominant and probably only real standard is the IEEE 802.15.4 specification. However, there have been efforts to use Bluetooth and Wi-Fi for sensor applications. In all the cases reported, Bluetooth and Wi-Fi were used in a non-standard way, in fact weaving the principles of IEEE 802.15.4 into their native implementation. It is nowadays widely accepted that the IEEE 802.15.4 offers the best basis for wireless sensor applications.
Even within the boundaries of standards, technology providers can discover and exploit differentiation opportunities. As an example, GreenPeak has developed Transceiver and Network Stack technology that is compliant to the IEEE 802.15.4 standard but includes additional functionalities that enable its use for ultra-low-power applications. An ultra-low-power application is defined as an application that is able to live off a coin cell battery or off energy harvested from the environment through a solar cell, a vibration energy harvester or any other environment energy converter. See Sidebar “Energy Harvesting Devices.”